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Sleep"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Sleep-disordered breathing (SDB) can range from habitual snoring to severe obstructive sleep apnea (OSA). A common characteristic of SDB in children is mouth breathing, yet it is commonly overlooked and inconsistently diagnosed. The primary aim of this study is to construct a deep learning algorithm in order to automatically detect mouth breathing events in children from polysomnography (PSG) recordings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The PSG of 20 subjects aged 10\u201313 years were used, 15 of which had reported snoring or presented high snoring and\/or high OSA values by scoring conducted by a sleep technologist, including mouth breathing events. The separately measured mouth and nasal pressure signals from the PSG were fed through convolutional neural networks to identify mouth breathing events.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The finalized model presented 93.5% accuracy, 97.8% precision, 89% true positive rate, and 2% false positive rate when applied to the validation data that was set aside from the training data. The model's performance decreased when applied to a second validation data set, indicating a need for a larger training set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results show the potential of deep neural networks in the analysis and classification of biological signals, and illustrates the usefulness of machine learning in sleep analysis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frsle.2023.1082996","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T17:03:14Z","timestamp":1676653394000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning for sleep analysis on children with sleep-disordered breathing: Automatic detection of mouth breathing events"],"prefix":"10.3389","volume":"2","author":[{"given":"J\u00f3na El\u00edsabet","family":"Sturlud\u00f3ttir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sigr\u00ed\u00f0ur","family":"Sigur\u00f0ard\u00f3ttir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Serwatko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erna S.","family":"Arnard\u00f3ttir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harald","family":"Hrubos-Str\u00f8m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael Valur","family":"Clausen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sigurveig","family":"Sigur\u00f0ard\u00f3ttir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda","family":"\u00d3skarsd\u00f3ttir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna Sigridur","family":"Islind","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.jsmc.2021.05.004","article-title":"The future of sleep measurements: a review and perspective","volume":"16","author":"Arnardottir","year":"2021","journal-title":"Sleep Med. 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